Estadistica Espacial Aplicada

Ejemplificacion con la base de Properati

Lectura del dataset y su estructura

# leo el archivo ar_properties 
library(tidyverse) # libreria para data wrangling
datos1a <- read_csv("ar_properties.csv") # Acá completen con su propio PATH al archivo

── Column specification ──────────────────────────────────────────────────────────────────────
cols(
  .default = col_character(),
  start_date = col_date(format = ""),
  end_date = col_date(format = ""),
  created_on = col_date(format = ""),
  lat = col_double(),
  lon = col_double(),
  l6 = col_logical(),
  rooms = col_double(),
  bedrooms = col_double(),
  bathrooms = col_double(),
  surface_total = col_double(),
  surface_covered = col_double(),
  price = col_double()
)
ℹ Use `spec()` for the full column specifications.

datos1a # veo la base

Aplicando filtros

Se seleccionan aquellos registros que pertenecen a Argentina y Capital Federal, cuyo precio esta en dolares (USD), el tipo de propiedad corresponde a Departamento y el tipo de operacion sea Venta.

datos1b <- datos1a %>% 
                   # Me quedo con los que pertenecen a Argentina y Capital Federal
            filter(l1 == "Argentina", 
                   l2 == "Capital Federal", 
                   # cuyo precio este en dolares 
                   currency == "USD", 
                   # propiedad tipo Departamento
                   property_type %in% c("Departamento"),
                   # operaciones de venta
                   operation_type == "Venta",
                   # acoto por precio y superficie
                   between(surface_covered,11,5000),between(price,1000,2e6)) %>% select(id,l3,surface_covered,price,lat,lon,rooms,bathrooms,bedrooms) %>% na.omit() %>% mutate(pm2=precio/sup) %>% rename(precio=price,barrio=l3,sup=surface_covered,ambientes=rooms,baths=bathrooms,cuartos=bedrooms) %>% na.omit()
# chequeo si el filtro se refleja correctamente en mi nuevo dataset datos1b
datos1b 

attach(datos1b) # pongo las variables en memoria

The following objects are masked from datos1b (pos = 3):

    barrio, id, lat, lon, pm2, precio, sup

The following objects are masked from datos1b (pos = 4):

    barrio, id, lat, lon, pm2, precio, sup

The following objects are masked from datos1b (pos = 5):

    barrio, id, lat, lon, pm2, precio, sup

The following objects are masked from datos1b (pos = 6):

    barrio, id, lat, lon, pm2, precio, sup

The following objects are masked from datos1b (pos = 8):

    barrio, id, lat, lon, pm2, precio, sup

The following objects are masked from datos1b (pos = 14):

    barrio, id, lat, lon, pm2, precio, sup

Cargo las librerias basicas para trabajar con datos espaciales

library(terra)
library(sf)
#vignette(package = "sf")
#vignette("sf1",package = "sf")
library("leaflet")
library(tmap)
library(OpenStreetMap)

Datos Vectoriales

Cargo deptos como Puntos

deptos.sf

Simple feature collection with 27276 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -58.53047 ymin: -34.69215 xmax: -58.357 ymax: -34.53613
CRS:           EPSG:4326

cargo calles de CABA

# cargo el shape de calles de CABA
calles.comp <- st_read("/home/andresfaral/Dropbox/Estadistica Espacial/callejero/")

Reading layer `callejero' from data source 
  `/home/andresfaral/Dropbox/Estadistica Espacial/callejero' using driver `ESRI Shapefile'
Simple feature collection with 30712 features and 29 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: -58.53244 ymin: -34.70574 xmax: -58.34191 ymax: -34.52947
CRS:           4326

# me quedo con algunos features
calles<-calles.comp[,c("id","tipo_c","nom_mapa","long")]
plot(calles)

# selecciono solo las avenidas
avenidas<-calles[calles$tipo_c=="AVENIDA",]
plot(avenidas)

nombres.ave<-avenidas$nom_mapa
# av rivadavia 
cuales<-grep("RIVADAVIA",nombres.ave)
rivadavia<-avenidas[cuales,]
plot(rivadavia)

# largo de la ave rivadavia
sum(rivadavia$long)

[1] 16623.84

# geometria de la ave rivadavia
rivadavia.geo<-rivadavia$geometry
# grafico con leaflet de avenidas
leaflet(avenidas) %>% addTiles() %>% addPolylines()

# grafico con leaflet de Rivadavia
leaflet(rivadavia) %>% addTiles() %>% addPolylines()

NA

Carga de Barrios CABA

# cargo barrios
barrios.comp <- st_read("/home/andresfaral/Dropbox/Estadistica Espacial/barrios/")

Reading layer `barrios_badata_wgs84' from data source 
  `/home/andresfaral/Dropbox/Estadistica Espacial/barrios' using driver `ESRI Shapefile'
Simple feature collection with 48 features and 5 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326

barrios.comp

Simple feature collection with 48 features and 5 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 10 features:
             BARRIO COMUNA PERIMETRO    AREA OBJETO                       geometry
1         CHACARITA     15  7724.853 3115707 BARRIO POLYGON ((-58.45282 -34.595...
2          PATERNAL     15  7087.513 2229829 BARRIO POLYGON ((-58.46558 -34.596...
3      VILLA CRESPO     15  8131.857 3615978 BARRIO POLYGON ((-58.42375 -34.597...
4  VILLA DEL PARQUE     11  7705.390 3399596 BARRIO POLYGON ((-58.49461 -34.614...
5           ALMAGRO      5  8537.901 4050752 BARRIO POLYGON ((-58.41287 -34.614...
6         CABALLITO      6 10990.964 6851029 BARRIO POLYGON ((-58.43061 -34.607...
7  VILLA SANTA RITA     11  5757.752 2153691 BARRIO POLYGON ((-58.48192 -34.624...
8      MONTE CASTRO     10  7361.036 2627481 BARRIO POLYGON ((-58.50349 -34.624...
9        VILLA REAL     10  5070.966 1334146 BARRIO POLYGON ((-58.52512 -34.614...
10           FLORES      7 15772.440 8590784 BARRIO POLYGON ((-58.46041 -34.656...

plot(barrios.comp)

# me quedo solo con feature de barrio
barrios<-barrios.comp[1]
barrios

Simple feature collection with 48 features and 1 field
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 10 features:
             BARRIO                       geometry
1         CHACARITA POLYGON ((-58.45282 -34.595...
2          PATERNAL POLYGON ((-58.46558 -34.596...
3      VILLA CRESPO POLYGON ((-58.42375 -34.597...
4  VILLA DEL PARQUE POLYGON ((-58.49461 -34.614...
5           ALMAGRO POLYGON ((-58.41287 -34.614...
6         CABALLITO POLYGON ((-58.43061 -34.607...
7  VILLA SANTA RITA POLYGON ((-58.48192 -34.624...
8      MONTE CASTRO POLYGON ((-58.50349 -34.624...
9        VILLA REAL POLYGON ((-58.52512 -34.614...
10           FLORES POLYGON ((-58.46041 -34.656...

plot(barrios)

# la geometria
barrios$geometry

Geometry set for 48 features 
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 5 geometries:

POLYGON ((-58.45282 -34.59599, -58.45366 -34.59...
POLYGON ((-58.46558 -34.59656, -58.46562 -34.59...
POLYGON ((-58.42375 -34.59783, -58.42495 -34.59...
POLYGON ((-58.49461 -34.61487, -58.49479 -34.61...
POLYGON ((-58.41287 -34.61412, -58.41282 -34.61...

class(barrios)

[1] "sf"         "data.frame"

plot(barrios[1:5,])

print(barrios.comp, n = 3)

Simple feature collection with 48 features and 5 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 3 features:
        BARRIO COMUNA PERIMETRO    AREA OBJETO                       geometry
1    CHACARITA     15  7724.853 3115707 BARRIO POLYGON ((-58.45282 -34.595...
2     PATERNAL     15  7087.513 2229829 BARRIO POLYGON ((-58.46558 -34.596...
3 VILLA CRESPO     15  8131.857 3615978 BARRIO POLYGON ((-58.42375 -34.597...

# union de barrios
union._barrios<-st_union(barrios)
union._barrios

Geometry set for 1 feature 
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326

POLYGON ((-58.35468 -34.62922, -58.35488 -34.62...

plot(union._barrios)

# grafico con leaflet
leaflet(barrios) %>% addTiles() %>% addPolygons()

Carga de Comunas CABA

comunas.comp <- st_read("/home/andresfaral/Dropbox/Estadistica Espacial/comunas/")

Reading layer `comunas_wgs84' from data source 
  `/home/andresfaral/Dropbox/Estadistica Espacial/comunas' using driver `ESRI Shapefile'
Simple feature collection with 15 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326

comunas.comp

Simple feature collection with 15 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 10 features:
   ID         OBJETO COMUNAS
1   1 LIMITE COMUNAL       2
2   3 LIMITE COMUNAL       6
3   6 LIMITE COMUNAL      10
4   7 LIMITE COMUNAL      11
5   8 LIMITE COMUNAL      12
6   9 LIMITE COMUNAL      14
7  11 LIMITE COMUNAL      15
8  15 LIMITE COMUNAL      13
9  13 LIMITE COMUNAL       4
10  2 LIMITE COMUNAL       5
                                                                           BARRIOS PERIMETRO
1                                                                         RECOLETA  21452.84
2                                                                        CABALLITO  10990.96
3  FLORESTA - MONTE CASTRO - VELEZ SARSFIELD - VERSALLES - VILLA LURO - VILLA REAL  18332.04
4          VILLA DEL PARQUE - VILLA  DEVOTO - VILLA GRAL. MITRE - VILLA SANTA RITA  19987.70
5                            COGHLAN - SAAVEDRA - VILLA PUEYRREDON - VILLA URQUIZA  17232.19
6                                                                          PALERMO  22126.53
7  AGRONOMIA - CHACARITA -  PARQUE CHAS -  PATERNAL - VILLA CRESPO - VILLA ORTUZAR  17832.59
8                                                    BELGRANO - COLEGIALES - NUÑEZ  26198.83
9                               BARRACAS - BOCA - NUEVA POMPEYA - PARQUE PATRICIOS  35423.28
10                                                                 ALMAGRO - BOEDO  12323.43
       AREA                       geometry
1   6317265 MULTIPOLYGON (((-58.38 -34....
2   6851029 MULTIPOLYGON (((-58.43061 -...
3  12656557 MULTIPOLYGON (((-58.48834 -...
4  14120042 MULTIPOLYGON (((-58.49838 -...
5  15570927 MULTIPOLYGON (((-58.50331 -...
6  15845871 MULTIPOLYGON (((-58.42676 -...
7  14322897 MULTIPOLYGON (((-58.42602 -...
8  14713213 MULTIPOLYGON (((-58.44033 -...
9  21684645 MULTIPOLYGON (((-58.38864 -...
10  6660603 MULTIPOLYGON (((-58.43333 -...

plot(comunas.comp)

comunas<-comunas.comp[1]
plot(comunas)

Grafico de deptos, avenidas, comunas y barrios

# todo
leaflet() %>% addTiles() %>% addPolygons(data=barrios,color = "green") %>% addPolygons(data=comunas,color = "red") %>% addCircles(data=deptos.sf,radius=0.01) %>% addPolylines(data=avenidas,color = "black")

# un poco
leaflet() %>% addTiles() %>% addPolygons(data=barrios,color = "green") %>% addPolygons(data=comunas,color = "red") %>% addCircles(data=deptos.sf[1:1000,],radius=0.01) %>% addPolylines(data=rivadavia,color = "black")

# barrios with ggplot
ggplot() + geom_sf(data = deptos.sf, size = 0.3,color="grey") + geom_sf(data = barrios, aes(fill = BARRIO), alpha = 0.5) + geom_sf(data=rivadavia)

# comunas with ggplot
ggplot() + geom_sf(data = deptos.sf, size = 0.3,color="grey") + geom_sf(data=comunas.comp, aes(fill = COMUNAS), alpha = 0.5) + geom_sf(data=rivadavia)

Manipulacion y Operacion con objetos espaciales

Operadores binarios o binary predicates

Que Comuna incluye a Versalles ?

#
sf::sf_use_s2(FALSE)

Spherical geometry (s2) switched off

vers<-barrios[barrios$BARRIO=="VERSALLES",]
com10<-comunas.comp[comunas.comp$COMUNAS==10,]
plot(com10$geometry)
plot(vers$geometry,add=T,col="green")

# esta versalles en la comuna 10 ?
st_contains(com10,vers,sparse = FALSE)

although coordinates are longitude/latitude, st_contains assumes that they are planar

     [,1]
[1,] TRUE

# que barrios estan en que comunas
test1 <- st_intersects(comunas.comp,barrios,sparse = FALSE)

although coordinates are longitude/latitude, st_intersects assumes that they are planar

test1

       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12] [,13] [,14]
 [1,] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [2,] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
 [3,] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [4,] FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
 [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [6,]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [7,]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [8,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE
[10,] FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
[11,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
[12,] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
[13,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
      [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28]
 [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
 [3,]  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
 [5,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE
 [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
 [8,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [9,] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[11,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
[12,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
[13,]  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
      [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42]
 [1,] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [2,] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [3,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE
 [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [5,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [6,] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [7,]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [8,]  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
 [9,] FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE
[10,] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[11,] FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[12,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[13,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE
[14,] FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[15,] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
      [,43] [,44] [,45] [,46] [,47] [,48]
 [1,]  TRUE FALSE  TRUE  TRUE FALSE FALSE
 [2,] FALSE FALSE FALSE FALSE FALSE FALSE
 [3,] FALSE FALSE FALSE FALSE FALSE FALSE
 [4,] FALSE FALSE FALSE FALSE FALSE FALSE
 [5,] FALSE  TRUE FALSE FALSE  TRUE FALSE
 [6,] FALSE  TRUE  TRUE FALSE FALSE FALSE
 [7,] FALSE  TRUE FALSE FALSE FALSE FALSE
 [8,] FALSE  TRUE FALSE FALSE  TRUE FALSE
 [9,] FALSE FALSE FALSE FALSE FALSE  TRUE
[10,] FALSE FALSE  TRUE FALSE FALSE FALSE
[11,] FALSE FALSE FALSE FALSE FALSE FALSE
[12,]  TRUE FALSE  TRUE FALSE FALSE FALSE
[13,] FALSE FALSE FALSE FALSE FALSE FALSE
[14,] FALSE FALSE FALSE FALSE FALSE FALSE
[15,]  TRUE FALSE  TRUE  TRUE FALSE  TRUE

Que deptos caen en versalles ?

test2 <- st_within(deptos.sf,vers,sparse = FALSE)

although coordinates are longitude/latitude, st_within assumes that they are planar

sum(test2)

[1] 25

plot(com10$geometry)
plot(vers$geometry,add=T,col="green")

plot(deptos.sf$geometry[test2],add=T,col="blue")

Que deptos estan a menos de 100 metros de Rivadavia

test3 <- st_is_within_distance(deptos.sf,rivadavia,dist = 500,sparse = FALSE)
dim(test3)

[1] 27276   169

class(test3)

[1] "matrix"

test3[1:10,1:6]

       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]
 [1,] FALSE FALSE FALSE FALSE FALSE FALSE
 [2,] FALSE FALSE FALSE FALSE FALSE FALSE
 [3,] FALSE FALSE FALSE FALSE FALSE FALSE
 [4,] FALSE FALSE FALSE FALSE FALSE FALSE
 [5,] FALSE FALSE FALSE FALSE FALSE FALSE
 [6,] FALSE FALSE FALSE FALSE FALSE FALSE
 [7,] FALSE FALSE FALSE FALSE FALSE FALSE
 [8,] FALSE FALSE FALSE FALSE FALSE FALSE
 [9,] FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE FALSE FALSE FALSE FALSE

cuales.deptos<-apply(test3,1,sum)
sum(cuales>0)

[1] 169

length(cuales.deptos)

[1] 27276

ggplot() + geom_sf(data = deptos.sf, size = 0.3,color="grey") + geom_sf(data = deptos.sf[cuales.deptos>0,], size = 0.5,color="blue") + geom_sf(data=rivadavia)

Combinacion de features

# uniendo tramos de rivadavia
rivadavia.unida<-st_combine(rivadavia)
rivadavia.unida

Geometry set for 1 feature 
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: -58.52928 ymin: -34.63935 xmax: -58.37038 ymax: -34.6072
CRS:           4326

MULTILINESTRING ((-58.47565 -34.63232, -58.4767...

ggplot() + geom_sf(data=comunas.comp, aes(fill = COMUNAS), alpha = 0.5) + geom_sf(data=rivadavia)

ggplot() + geom_sf(data=comunas.comp, aes(fill = COMUNAS), alpha = 0.5) + geom_sf(data=rivadavia.unida)

# combinando barrios
barrios.unidos<-st_combine(barrios)
barrios.unidos

Geometry set for 1 feature 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326

MULTIPOLYGON (((-58.45282 -34.59599, -58.45366 ...

plot(barrios.unidos)

Que avenidas cortan Rivadavia

# Que avenidas cortan rivadavia
NoRivadavia<-avenidas[avenidas$nom_mapa!="AV. RIVADAVIA",]
test4<-st_intersects(NoRivadavia,rivadavia,sparse = FALSE)

although coordinates are longitude/latitude, st_intersects assumes that they are planar

dim(test4)

[1] 6589  169

cuales.cortan<-apply(test4,1,sum)
nombres.cortan<-NoRivadavia$nom_mapa[cuales.cortan>0]
ggplot() + geom_sf(data=NoRivadavia[NoRivadavia$nom_mapa%in%nombres.cortan,],color="blue")  + geom_sf(data=rivadavia,color="red")

Que avenidas estan cerca Rivadavia

# Que avenidas cortan rivadavia
NoRivadavia<-avenidas[avenidas$nom_mapa!="AV. RIVADAVIA",]
test7<-st_is_within_distance(NoRivadavia,rivadavia,dist=1000,sparse = FALSE)
dim(test7)

[1] 6589  169

cuales.cortan<-apply(test7,1,sum)
nombres.cortan<-NoRivadavia$nom_mapa[cuales.cortan>0]
ggplot() + geom_sf(data=NoRivadavia[NoRivadavia$nom_mapa%in%nombres.cortan,],color="blue")  + geom_sf(data=rivadavia,color="red")

Que avenidas hay en Caballito

test5<-st_intersects(barrios[barrios$BARRIO=="CABALLITO",],avenidas,sparse = FALSE)

although coordinates are longitude/latitude, st_intersects assumes that they are planar

dim(test5)

[1]    1 6758

test5<-as.numeric(test5)
head(test5)

[1] 0 0 0 0 0 0

ggplot() + geom_sf(data=avenidas[test5>0,],color="blue")  + geom_sf(data = barrios[barrios$BARRIO=="CABALLITO",], aes(fill = BARRIO), alpha = 0.5)

Que barrios son contiguos a Caballito ?

# falla al norte
test6<-st_touches(barrios,barrios[barrios$BARRIO=="CABALLITO",],sparse = FALSE)

although coordinates are longitude/latitude, st_touches assumes that they are planar

# funciona ok
test6<-st_is_within_distance(barrios,barrios[barrios$BARRIO=="CABALLITO",],dist=100,sparse = FALSE)
dim(test6)

[1] 48  1

test6<-as.numeric(test6)
head(test6)

[1] 0 1 1 0 1 1

ggplot() + geom_sf(data = barrios[barrios$BARRIO=="CABALLITO",], aes(fill = BARRIO), alpha = 0.5)  + geom_sf(data = barrios[test6>0,], aes(fill = BARRIO), alpha = 0.5)

Que deptos estan sobre avenidas ? LENTO !!!!

# test8 <- st_is_within_distance(deptos.sf,avenidas,dist = 10,sparse = FALSE)
# dim(test8)
# cuales.deptos<-apply(test8,1,sum)
# sum(cuales.deptos>0)
# ggplot() + geom_sf(data = deptos.sf, size = 0.3,color="grey") + geom_sf(data = deptos.sf[cuales.deptos>0,], size = 0.5,color="blue") 

Cuantos deptos hay en cada barrio ? graficos con tmap

require(tmap)
deptos_in_barrios <- st_join(deptos.sf, barrios, join = st_within)

although coordinates are longitude/latitude, st_within assumes that they are planar

deptos_in_barrios

Simple feature collection with 27276 features and 12 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -58.53047 ymin: -34.69215 xmax: -58.357 ymax: -34.53613
CRS:           EPSG:4326

deptos_barrios_count <- count(as_tibble(deptos_in_barrios), BARRIO)
deptos_barrios_count

barrios_con_deptos <- left_join(barrios, deptos_barrios_count)

Joining, by = "BARRIO"

barrios_con_deptos

Simple feature collection with 48 features and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 10 features:
             BARRIO    n                       geometry
1         CHACARITA  359 POLYGON ((-58.45282 -34.595...
2          PATERNAL  164 POLYGON ((-58.46558 -34.596...
3      VILLA CRESPO 2190 POLYGON ((-58.42375 -34.597...
4  VILLA DEL PARQUE  260 POLYGON ((-58.49461 -34.614...
5           ALMAGRO 2707 POLYGON ((-58.41287 -34.614...
6         CABALLITO 1966 POLYGON ((-58.43061 -34.607...
7  VILLA SANTA RITA  180 POLYGON ((-58.48192 -34.624...
8      MONTE CASTRO  188 POLYGON ((-58.50349 -34.624...
9        VILLA REAL   33 POLYGON ((-58.52512 -34.614...
10           FLORES  712 POLYGON ((-58.46041 -34.656...

tmap_mode("view")

tmap mode set to interactive viewing

tm_shape(barrios_con_deptos) +
  tm_fill(
    col = "n",
    palette = "Greens",
    style = "cont",
    contrast = c(0.1, 1),
    title = "Deptos por Barrio",
    id = "boro_ct2010",
    showNA = FALSE,
    alpha = 0.8) +
  tm_borders(col = "darkgray", lwd = 0.7)

Calculando la Capsula Convexa de los deptos

# solo los deptos
ggplot() + geom_sf(data=deptos.sf,size=0.1)

deptos.sf.unidos<-st_union(deptos.sf)

although coordinates are longitude/latitude, st_union assumes that they are planar

capsula<-st_convex_hull(deptos.sf.unidos)
capsula

Geometry set for 1 feature 
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53047 ymin: -34.69215 xmax: -58.357 ymax: -34.53613
CRS:           EPSG:4326

POLYGON ((-58.46401 -34.69215, -58.46972 -34.69...

ggplot()  + geom_sf(data=capsula,color="blue") + geom_sf(data=deptos.sf,size=0.1)

Calculemos las areas de los barrios

barrios
barrios2 <- barrios %>% mutate(AREA=st_area(barrios)) %>% arrange(-AREA)# agrego el area

Distancia entre barrios

barrios2

Simple feature collection with 48 features and 2 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -58.53152 ymin: -34.70529 xmax: -58.33515 ymax: -34.52649
CRS:           4326
First 10 features:
          BARRIO           AREA                       geometry
1        PALERMO 15845872 [m^2] POLYGON ((-58.42676 -34.552...
2   VILLA LUGANO  9296146 [m^2] POLYGON ((-58.46571 -34.659...
3  VILLA SOLDATI  8688167 [m^2] POLYGON ((-58.44913 -34.651...
4         FLORES  8590788 [m^2] POLYGON ((-58.46041 -34.656...
5       BELGRANO  7999243 [m^2] POLYGON ((-58.45057 -34.535...
6       BARRACAS  7953447 [m^2] POLYGON ((-58.37034 -34.632...
7      MATADEROS  7398180 [m^2] POLYGON ((-58.50252 -34.674...
8      CABALLITO  6851031 [m^2] POLYGON ((-58.43061 -34.607...
9   VILLA DEVOTO  6404480 [m^2] POLYGON ((-58.50831 -34.613...
10      RECOLETA  6317261 [m^2] POLYGON ((-58.38 -34.57002,...

st_distance(barrios[barrios$BARRIO=="BELGRANO",],barrios[barrios$BARRIO=="FLORES",])

Units: [m]
         [,1]
[1,] 4327.646

st_distance(barrios[barrios$BARRIO=="CABALLITO",],barrios[barrios$BARRIO=="FLORES",])

Units: [m]
     [,1]
[1,]    0

distan<-st_nearest_points(barrios[barrios$BARRIO=="BELGRANO",],barrios[barrios$BARRIO=="FLORES",])

although coordinates are longitude/latitude, st_nearest_points assumes that they are planar

ggplot() + geom_sf(data = barrios, alpha = 0.5) + geom_sf(data = barrios[barrios$BARRIO=="FLORES",], aes(fill = BARRIO)) + geom_sf(data = barrios[barrios$BARRIO=="BELGRANO",], aes(fill = BARRIO)) + geom_sf(data=distan,col="blue")

Analisis de Espacio Verde Publico

Cual es el barrio con mas Espacio Verde ?

Estaciones de Subte

Calculemos la distancia de cada depto a la estacion de subte mas cercana

deptos.sf

Simple feature collection with 27276 features and 11 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -58.53047 ymin: -34.69215 xmax: -58.357 ymax: -34.53613
CRS:           EPSG:4326

Moelado del precio de los Depaertamentos

Moelo lineal basico